Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2404.07569.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:24:07.093198Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-10T16:57:24.455369Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 84786de6-e0ec-4d43-b4c7-b602e670945b · inbound
Generative AI for Autonomous Driving: A Review cites this paper.
Generative AI for Autonomous Driving: A Review Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?
Reference 208
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:24:07.093198Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9574cd77-c4ac-4020-8d2b-9e8ff9b13d8e · inbound
LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios cites this paper.
LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T13:21:35.754388Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a8afbd98-912a-4981-a046-67a490a1e3d6 · inbound
Shift & Drift: A Zero-Shot Benchmark for Generalizable and Robust Autonomous Driving Motion Planning cites this paper.
Shift & Drift: A Zero-Shot Benchmark for Generalizable and Robust Autonomous Driving Motion Planning Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?
Reference 35
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-10T16:57:24.456947Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.